Medical Information Technology, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany.
Department of Anaesthesiology and Intensive Care Medicine, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
Physiol Meas. 2021 Aug 27;42(8). doi: 10.1088/1361-6579/ac0e84.
. Electrical impedance tomography (EIT) for lung perfusion imaging is attracting considerable interest in intensive care, as it might open up entirely new ways to adjust ventilation therapy. A promising technique is bolus injection of a conductive indicator to the central venous catheter, which yields the indicator-based signal (IBS). Lung perfusion images are then typically obtained from the IBS using the maximum slope technique. However, the low spatial resolution of EIT results in a partial volume effect (PVE), which requires further processing to avoid regional bias.. In this work, we repose the extraction of lung perfusion images from the IBS as a source separation problem to account for the PVE. We then propose a model-based algorithm, called gamma decomposition (GD), to derive an efficient solution. The GD algorithm uses a signal model to transform the IBS into a parameter space where the source signals of heart and lung are separable by clustering in space and time. Subsequently, it reconstructs lung model signals from which lung perfusion images are unambiguously extracted.. We evaluate the GD algorithm on EIT data of a prospective animal trial with eight pigs. The results show that it enables lung perfusion imaging using EIT at different stages of regional impairment. Furthermore, parameters of the source signals seem to represent physiological properties of the cardio-pulmonary system.. This work represents an important advance in IBS processing that will likely reduce bias of EIT perfusion images and thus eventually enable imaging of regional ventilation/perfusion (V/Q) ratio.
. 用于肺部灌注成像的电阻抗断层成像（EIT）在重症监护领域引起了相当大的兴趣，因为它可能为调整通气治疗开辟全新的途径。一种很有前途的技术是将导电指示剂通过中心静脉导管推注，从而产生基于指示剂的信号（IBS）。然后通常使用最大斜率技术从 IBS 中获取肺部灌注图像。然而，EIT 的低空间分辨率导致部分容积效应（PVE），这需要进一步处理以避免区域偏差。在这项工作中，我们将从 IBS 中提取肺部灌注图像重新作为源分离问题来考虑 PVE。然后，我们提出了一种基于模型的算法，称为伽马分解（GD），以得出有效的解决方案。GD 算法使用信号模型将 IBS 转换为参数空间，其中心脏和肺部的源信号可以通过空间和时间上的聚类来分离。随后，它从肺部模型信号中重建，从而明确地提取出肺部灌注图像。我们在 8 头猪的前瞻性动物试验的 EIT 数据上评估了 GD 算法。结果表明，它可以在区域性损伤的不同阶段使用 EIT 进行肺部灌注成像。此外，源信号的参数似乎代表心肺系统的生理特性。这项工作代表了 IBS 处理方面的重要进展，有望减少 EIT 灌注图像的偏差，从而最终实现区域通气/灌注（V/Q）比的成像。
